Processing device utilizing polynomial-based signature subspace for efficient generation of deduplication estimate

ABSTRACT

An apparatus in one embodiment comprises at least one processing device comprising a processor coupled to a memory. The processing device is configured to identify a dataset to be scanned to generate a deduplication estimate for that dataset, to designate a subset inclusion characteristic to be utilized in the scan, and for each of a plurality of pages of the dataset, to scan the page, where scanning the page includes computing a polynomial-based signature for the page, determining whether or not the polynomial-based signature satisfies the designated subset inclusion characteristic, and responsive to the polynomial-based signature satisfying the designated subset inclusion characteristic, computing a content-based signature for the page and updating a corresponding entry of a deduplication estimate table for the dataset based at least in part on the content-based signature. The processing device generates the deduplication estimate for the dataset based at least in part on contents of the deduplication estimate table.

FIELD

The field relates generally to information processing systems, and moreparticularly to storage in information processing systems.

BACKGROUND

In many information processing systems, it is desirable to implementdeduplication functionality in order to ensure that the same data is notrepeatedly stored in a duplicative manner that consumes excessivestorage capacity. Deduplication decisions in some systems are guided bydeduplication estimates, which can provide an indication of the amountof storage capacity that would be released if deduplication wereperformed. However, conventional approaches to deduplication can beproblematic. For example, in some systems, generating a deduplicationestimate for a set of logical storage volumes can require substantialcomputational and memory resources. These and other relateddeduplication inefficiencies can significantly undermine systemperformance.

SUMMARY

Illustrative embodiments provide techniques for efficient generation ofdeduplication estimates for datasets of a storage system throughutilization of polynomial-based signature subspaces in scanning pages ofthe datasets. Such arrangements can considerably reduce the amounts ofcomputational and memory resources that are required to generatededuplication estimates, thereby leading to improved deduplicationdecisions and associated improvements in system performance.

In one embodiment, an apparatus comprises at least one processing devicecomprising a processor coupled to a memory. The processing device isconfigured to identify a dataset to be scanned to generate adeduplication estimate for that dataset, to designate a subset inclusioncharacteristic to be utilized in the scan, and for each of a pluralityof pages of the dataset, to scan the page by computing apolynomial-based signature for the page, determining whether or not thepolynomial-based signature satisfies the designated subset inclusioncharacteristic, and responsive to the polynomial-based signaturesatisfying the designated subset inclusion characteristic, computing acontent-based signature for the page and updating a corresponding entryof a deduplication estimate table for the dataset based at least in parton the content-based signature. The processing device generates thededuplication estimate for the dataset based at least in part oncontents of the deduplication estimate table.

The processing device in some embodiments is implemented in a hostdevice configured to communicate over a network with a storage systemthat stores the dataset. In other embodiments, the processing device isimplemented in the storage system itself. These are only examples, andalternative implementations are possible.

The dataset illustratively comprises a set of one or more logicalstorage volumes, with each of the one or more logical storage volumescomprising at least a portion of a physical storage space of the storagesystem.

The deduplication estimate table in some embodiments comprises aplurality of entries for respective ones of the pages, with each of theentries being configured to include a page identifier that comprisesless than an entire content-based signature of its corresponding page.For example, the page identifier in a corresponding one of the entriesmay comprise a specified number of initial bytes of the content-basedsignature of that page.

The designated subset inclusion characteristic may specify thatapplication of a designated function to the polynomial-based signatureproduces a particular result.

In some embodiments, the polynomial-based signature comprises an n-bitcyclic redundancy check (CRC) value, such as a 32-bit CRC value. In anarrangement of this type, the designated subset inclusion characteristicmay specify that performing a designated modulo arithmetic operation onthe n-bit CRC value produces a particular value. Other types ofpolynomial-based signatures can be used in other embodiments.

In some embodiments, updating a corresponding entry of the deduplicationestimate table for a given one of the pages of the dataset proceeds asfollows. If a page identifier of the given page is not already presentin the deduplication estimate table, the page identifier is insertedinto the deduplication estimate table and an associated counter is setto an initial value. On the other hand, if the page identifier of thegiven page is already present in the deduplication estimate table, itsassociated counter is incremented. Other types of updating processes anddeduplication estimate table configurations may be used in otherembodiments.

In some embodiments, generating the deduplication estimate for thedataset based at least in part on contents of the deduplication estimatetable further comprises computing a partial deduplication estimate basedat least in part on values of counters associated with respectiveentries of the deduplication estimate table, and scaling the partialdeduplication estimate to obtain the deduplication estimate for thedataset.

The processing device may be configured in some embodiments to generateone or more additional deduplication estimates for respective ones ofone or more additional datasets, and to select a particular one of thedatasets for deduplication based at least in part on their respectivededuplication estimates.

Additional or alternative operations may be performed based at least inpart on a generated deduplication estimate. For example, the processingdevice may be configured to adjust one or more characteristics of astorage configuration of a dataset based at least in part on thededuplication estimate generated for the dataset.

These and other illustrative embodiments include, without limitation,apparatus, systems, methods and processor-readable storage media.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of an information processing system comprisinga host device configured with functionality for efficient generation ofdeduplication estimates for datasets in an illustrative embodiment.

FIG. 2 is a block diagram of an information processing system comprisinga storage system configured with functionality for efficient generationof deduplication estimates for datasets in an illustrative embodiment.

FIG. 3 is a flow diagram of a process for efficient generation ofdeduplication estimates for datasets in an illustrative embodiment.

FIG. 4 shows an example of a deduplication estimate table in anillustrative embodiment.

FIG. 5 shows a content addressable storage system having a distributedstorage controller configured with functionality for efficientgeneration of deduplication estimates for datasets in an illustrativeembodiment.

FIGS. 6 and 7 show examples of processing platforms that may be utilizedto implement at least a portion of an information processing system inillustrative embodiments.

DETAILED DESCRIPTION

Illustrative embodiments will be described herein with reference toexemplary information processing systems and associated computers,servers, storage devices and other processing devices. It is to beappreciated, however, that these and other embodiments are notrestricted to the particular illustrative system and deviceconfigurations shown. Accordingly, the term “information processingsystem” as used herein is intended to be broadly construed, so as toencompass, for example, processing systems comprising cloud computingand storage systems, as well as other types of processing systemscomprising various combinations of physical and virtual processingresources. An information processing system may therefore comprise, forexample, at least one data center or other cloud-based system thatincludes one or more clouds hosting multiple tenants that share cloudresources. Numerous different types of enterprise computing and storagesystems are also encompassed by the term “information processing system”as that term is broadly used herein.

FIG. 1 shows an information processing system 100 configured inaccordance with an illustrative embodiment. The information processingsystem 100 comprises a host device 102, which may comprise one of aplurality of host devices of a computer system. The host device 102communicates over a network 104 with first and second storage systems105-1 and 105-2, also denoted as Storage System 1 and Storage System 2,respectively. The storage systems 105-1 and 105-2 are collectivelyreferred to herein as storage systems 105. The host device 102 andstorage systems 105 may be part of an enterprise computing and storagesystem, a cloud-based system or another type of system.

The host device 102 and storage systems 105 illustratively compriserespective processing devices of one or more processing platforms. Forexample, the host device 102 and the storage systems 105 can eachcomprise one or more processing devices each having a processor and amemory, possibly implementing virtual machines and/or containers,although numerous other configurations are possible.

The host device 102 and the storage systems 105 can additionally oralternatively be part of cloud infrastructure such as an Amazon WebServices (AWS) system. Other examples of cloud-based systems that can beused to provide one or more of host device 102 and storage systems 105include Google Cloud Platform (GCP) and Microsoft Azure.

The host device 102 is configured to write data to and read data fromthe storage systems 105. The host device 102 and the storage systems 105may be implemented on a common processing platform, or on separateprocessing platforms. A wide variety of other types of host devices canbe used in other embodiments.

The host device 102 in some embodiments illustratively provides computeservices such as execution of one or more applications on behalf of eachof one or more users associated with the host device 102.

The term “user” herein is intended to be broadly construed so as toencompass numerous arrangements of human, hardware, software or firmwareentities, as well as combinations of such entities. Compute and/orstorage services may be provided for users under a platform-as-a-service(PaaS) model, although it is to be appreciated that numerous other cloudinfrastructure arrangements could be used. Also, illustrativeembodiments can be implemented outside of the cloud infrastructurecontext, as in the case of a stand-alone computing and storage systemimplemented within a given enterprise.

The network 104 is assumed to comprise a portion of a global computernetwork such as the Internet, although other types of networks can bepart of the network 104, including a wide area network (WAN), a localarea network (LAN), a satellite network, a telephone or cable network, acellular network, a wireless network such as a WiFi or WiMAX network, orvarious portions or combinations of these and other types of networks.The network 104 in some embodiments therefore comprises combinations ofmultiple different types of networks each comprising processing devicesconfigured to communicate using Internet Protocol (IP) or othercommunication protocols.

As a more particular example, some embodiments may utilize one or morehigh-speed local networks in which associated processing devicescommunicate with one another utilizing Peripheral Component Interconnectexpress (PCIe) cards of those devices, and networking protocols such asInfiniBand, Gigabit Ethernet or Fibre Channel. Numerous alternativenetworking arrangements are possible in a given embodiment, as will beappreciated by those skilled in the art.

The storage systems 105 are accessible to the host device over thenetwork 104. The storage system 105-1 comprises a plurality of storagedevices 106-1 and an associated storage controller 108-1. Similarly, thestorage system 105-2 comprises a plurality of storage devices 106-2 andan associated storage controller 108-2. The storage devices 106-1 and106-2 are collectively referred to herein as storage devices 106. Thestorage controllers 108-1 and 108-2 are collectively referred to asstorage controllers 108. The storage devices 106-1 and 106-2 storerespective datasets 110-1 and 110-2, collectively datasets 110.

The storage devices 106 illustratively comprise solid state drives(SSDs). Such SSDs are implemented using non-volatile memory (NVM)devices such as flash memory. Other types of NVM devices that can beused to implement at least a portion of the storage devices 106 includenon-volatile random access memory (NVRAM), phase-change RAM (PC-RAM) andmagnetic RAM (MRAM). These and various combinations of multipledifferent types of NVM devices may also be used.

However, it is to be appreciated that other types of storage devices canbe used in other embodiments. For example, a given storage system as theterm is broadly used herein can include a combination of different typesof storage devices, as in the case of a multi-tier storage systemcomprising a flash-based fast tier and a disk-based capacity tier. Insuch an embodiment, each of the fast tier and the capacity tier of themulti-tier storage system comprises a plurality of storage devices withdifferent types of storage devices being used in different ones of thestorage tiers. For example, the fast tier may comprise flash driveswhile the capacity tier comprises hard disk drives. The particularstorage devices used in a given storage tier may be varied in otherembodiments, and multiple distinct storage device types may be usedwithin a single storage tier. The term “storage device” as used hereinis intended to be broadly construed, so as to encompass, for example,flash drives, solid state drives, hard disk drives, hybrid drives orother types of storage devices.

In some embodiments, at least one of the storage systems 105illustratively comprises a scale-out all-flash content addressablestorage array such as an XtremIO™ storage array from Dell EMC ofHopkinton, Mass. Other types of storage arrays, including by way ofexample VNX® and Symmetrix VMAX® storage arrays also from Dell EMC, canbe used to implement one or both of storage systems 105 in otherembodiments.

The term “storage system” as used herein is therefore intended to bebroadly construed, and should not be viewed as being limited to contentaddressable storage systems or flash-based storage systems. A givenstorage system as the term is broadly used herein can comprise, forexample, network-attached storage (NAS), storage area networks (SANs),direct-attached storage (DAS) and distributed DAS, as well ascombinations of these and other storage types, includingsoftware-defined storage.

Other particular types of storage products that can be used inimplementing storage systems 105 in illustrative embodiments includeall-flash and hybrid flash storage arrays such as Unity™,software-defined storage products such as ScaleIO™ and ViPR®, cloudstorage products such as Elastic Cloud Storage (ECS), object-basedstorage products such as Atmos®, and scale-out NAS clusters comprisingIsilon® platform nodes and associated accelerators, all from Dell EMC.Combinations of multiple ones of these and other storage products canalso be used in implementing a given storage system in an illustrativeembodiment.

The host device 102 in the FIG. 1 embodiment includes deduplicationcontrol logic and associated deduplication estimate tables, collectivelyrepresented by module 112. The deduplication control logic is configuredto control performance of a deduplication estimate generation processsuch as that illustrated in the flow diagram of FIG. 3. Thededuplication estimate tables are maintained in a memory of the hostdevice 102 under the control of the deduplication control logic. Anexample deduplication estimate table format will be described below inconjunction with FIG. 4. The host device 102 further comprises acontent-based signature computation module 114, and should also beunderstood to include additional modules and other components typicallyfound in conventional implementations of computers, servers or otherhost devices, although such additional modules and other components areomitted from the figure for clarity and simplicity of illustration.

The host device 102 via its deduplication control logic is configured toidentify a dataset to be scanned to generate a deduplication estimatefor that dataset, to designate a subset inclusion characteristic to beutilized in the scan, and for each of a plurality of pages of thedataset, to scan the page. Scanning the page illustratively comprisescomputing a polynomial-based signature for the page, determining whetheror not the polynomial-based signature satisfies the designated subsetinclusion characteristic, and responsive to the polynomial-basedsignature satisfying the designated subset inclusion characteristic,computing a content-based signature for the page in the content-basedsignature computation module 114 and updating a corresponding entry of adeduplication estimate table for the dataset based at least in part onthe content-based signature. The host device 102 then generates thededuplication estimate for the dataset based at least in part oncontents of the deduplication estimate table.

For example, in some implementations of the FIG. 1 embodiment, the firststorage system 105-1 may comprise a traditional storage array withoutcontent-based deduplication functionality while the second storagesystem 105-2 comprises a content addressable storage array withcontent-based deduplication functionality. In such an arrangement, thehost device 102 can generate deduplication estimates for different onesof the datasets 110-1 of the first storage system 105-1 and use theresulting deduplication estimates to prioritize particular ones of thedatasets for migration to the second storage system 105-2. Accordingly,the host device 102 can implement an automated migration process thatobtains deduplication estimates for different ones of the datasets 110-1and migrates only those datasets that are likely to achieve at least athreshold level of deduplication upon migration to the second storagesystem 105-2. A given dataset when stored in the first storage system105-1 may therefore have significant unexploited deduplication potentialthat can be achieved upon migration to the second storage system 105-2.The generation of deduplication estimates in the host device 102 cansupport these and numerous other use cases relating to storageconfiguration in system 100.

A given dataset to be scanned by the host device 102 to generate adeduplication estimate illustratively comprises a set of one or morelogical units (LUNs) or other logical storage volumes of a particularone of the storage systems 105. However, the term “dataset” as usedherein is intended to be broadly construed, and may include other typesand arrangements of data, including snapshots, or portions thereof.

The deduplication estimate table illustratively comprises a plurality ofentries for respective ones of the pages, with each of the entries beingconfigured to include a page identifier that comprises less than anentire content-based signature of its corresponding page. The entriesalso include respective counters for the pages having page identifiersin the table. The counters are used to track the number of times thatpages having the corresponding page identifier appear in the datasetbeing scanned.

The designated subset inclusion characteristic illustratively specifiesthat application of a designated function to a polynomial-basedsignature computed for a given page of the dataset produces a particularresult. For example, in some embodiments, the polynomial-based signatureof the page comprises an n-bit CRC value, such as a 32-bit CRC value,and the designated subset inclusion characteristic specifies thatperforming a designated modulo arithmetic operation on the n-bit CRCvalue produces a particular value. The subset inclusion characteristiceffectively establishes a particular subspace of a total scan spacecomprising all possible polynomial-based signatures. Such CRCcomputation functionality is assumed to be implemented as part of thededuplication control logic of module 112 of host device 102.

The content-based signatures illustratively comprise hash digests oftheir respective pages, each generated by application of a hash functionsuch as the well-known Secure Hashing Algorithm 1 (SHA1) to itscorresponding page. It is also possible that other types ofcontent-based signatures may be used, such as hash handles of the typedescribed elsewhere herein. A given content-based signature inillustrative embodiments is unique to the particular content of the pagefrom which it is generated, such that two pages with exactly the samecontent will have the same content-based signature, while two pages withdifferent content will have different content-based signatures.

The polynomial-based signatures are substantially less costly to computein terms of computational resources than the content-based signatures.For example, in some embodiments the polynomial-based signatures do notguarantee unique signatures for pages having different content, while asnoted above the content-based signatures do guarantee unique signaturesfor pages having different content.

It is therefore possible that pages having different content in someembodiments can have the same polynomial-based signature. For example,in computing the polynomial-based signature of a given page, thededuplication control logic of the host device 102 may utilize only adesignated portion of the page content, such as the first 100 bytes ofthe page, rather than the entire content of that page. Thecomputationally inexpensive polynomial-based signatures are used toidentify in a particularly efficient manner those pages of the datasetthat satisfy the designated subset inclusion characteristic.

As one example, the polynomial-based signatures may be generated byapplying a 32-bit CRC to the first 100 bytes of each page, and eachpolynomial-based signature is tested against the designated subsetinclusion characteristic by applying a modulo 256 arithmetic operationto that signature and determining if the result matches at least oneparticular specified value. Adjustments can be made in one or more ofthe number of page bytes used to generate the polynomial-basedsignatures, the type of modulo arithmetic operation applied and thenumber of specified matching values in order to control the size of thesubspace established by the designated subset inclusion characteristic.

In updating a corresponding entry of the deduplication estimate tablefor a given one of the pages of the dataset, the host device 102 firstdetermines if a page identifier of the given page is already present inthe deduplication estimate table. If the page identifier is not alreadypresent in the deduplication estimate table, the host device 102 insertsthe page identifier into the deduplication estimate table and sets theassociated counter for that entry to an initial value, typically a valueof one. If the page identifier is already present in the deduplicationestimate table, the host device 102 increments the associated counterfor that entry, typically by increasing its current value by one. Thepage identifier illustratively comprises a designated number of initialbytes of the content-based signature of that page.

The polynomial-based signatures in many cases are substantiallyuniformly distributed over the total scan space of possiblepolynomial-based signature values. As a result, the subspace establishedby the designated subset inclusion characteristic is also substantiallyuniformly distributed. The size of the subspace can be controlled in astraightforward manner by simply adjusting the subset inclusioncharacteristic as noted above.

The ratio of the subspace established by the subset inclusioncharacteristic to the total scan space of all possible polynomial-basedsignatures more particularly provides a sampling ratio for the scanningprocess.

In some embodiments, the page identifier comprises a designated numberof initial bytes of the content-based signature for that page, where thedesignated number is greater than or equal to four. As a more particularexample, the designated number may be five, such that the pageidentifier comprises five initial bytes. Other page identifier lengthsand configurations may be used in other embodiments.

The host device 102 may be configured to adjust at least one of anamount of computational resources and an amount of memory resources tobe utilized in the scan of the dataset at least in part by altering alength of the page identifier.

As another example, the host device 102 can trade off accuracy of thededuplication estimate with the amount of computational or memoryresources required to generate the estimate. For example, the hostdevice 102 can select a length of the page identifier in order toachieve a rate of false positives in the deduplication estimate that isless than a specified maximum rate of false positives.

In some embodiments, generating the deduplication estimate for a datasetbased at least in part on contents of the deduplication estimate tablefurther comprises computing a partial deduplication estimate based atleast in part on values of the counters associated with respectiveentries of the deduplication estimate table, and scaling the partialdeduplication estimate to obtain the deduplication estimate for thedataset. For example, the counter values can be summed and the resultingpartial deduplication estimate scaled through multiplication by aninverse of the above-noted sampling ratio.

The deduplication estimate in some embodiments is in the form of adeduplication ratio, which indicates a potential reduction in size ofthe dataset if it were subject to a deduplication operation. Other typesof deduplication estimates can be used in other embodiments, and suchestimates therefore should not be viewed as limited to deduplicationratios or any other particular estimate format.

Various automated actions may be taken in at least one of the hostdevice 102 and the storage systems 105 based at least in part ondeduplication estimates generated in the manner described above.

For example, the host device 102 can adjust one or more characteristicsof a storage configuration of a given dataset based at least in part onthe deduplication estimate generated for the dataset. In the presentembodiment, this can include migrating the dataset from the firststorage system 105-1 to the second storage system 105-2, or vice versa.

As another example, the host device 102 can generate multiplededuplication estimates for respective datasets, and select a particularone of the datasets for deduplication based at least in part on theirrespective deduplication estimates. The host device 102 can send acommand to one of the storage systems 105 directing that deduplicationbe performed on the selected dataset in that storage system, assumingthat the commanded storage system supports deduplication functionality.

The illustrative embodiments described above avoid excessive consumptionof computational and memory resources that could otherwise result if thecontent-based signatures for all of the pages in the dataset had to bestored and compared in conjunction with scanning the dataset. Forexample, illustrative embodiments provide significant performanceadvantages over an arrangement which computes the content-basedsignature of each page of the dataset and determines for each of thecontent-based signatures how many times the exact same content-basedsignature appears. Such an arrangement would not only require largeamounts of memory to store a table having a separate entry for each fullcontent-based signature and its associated counter, but would also bevery inefficient in terms of processor and cache utilization. This isdue at least in part to the typical random nature of the content-basedsignatures, which would tend to result in frequent memory cache missesand associated accesses to slower storage resources in conjunction withthe scan. A possible alternative approach would be to scan only aportion of the dataset, but such an approach would clearly not produceaccurate deduplication estimates. For example, consider a simple datasethaving a sequence of pages with content of the form ABCDABCD. In thisexample, even if one were to select either the entire first half or theentire second half of the dataset for scanning, the resultingdeduplication estimate would be highly inaccurate, erroneouslyindicating zero deduplication potential.

These and other issues are addressed and overcome by the illustrativeembodiments, which as described previously utilize a designated subsetinclusion characteristic that is applied to computationally inexpensivepolynomial-based signatures to establish a subspace of the total scanspace. Such an arrangement can be used to generate highly accuratededuplication estimates using substantially reduced amounts ofcomputational and storage resources. The partial deduplication estimatefor the subspace is scaled to infer the deduplication estimate for thefull dataset. With reference again to the simple example of the datasethaving a sequence of pages with content of the form ABCDABCD, use of adesignated subset inclusion characteristic using polynomial-basedsignatures common to pages with content A would result in adeduplication estimate indicating potential deduplication by a factor of2, accurately reflecting that the size of the dataset can be reduced inhalf through deduplication.

In some embodiments, a process for generating a deduplication estimatein the system 100 includes the following operations:

1. Determine the size of the dataset to be scanned and what portion ofthe dataset should be sampled to provide an acceptable margin of errorin the deduplication estimate.

2. Determine the subset inclusion characteristic for the desiredsampling. For example, to establish a subspace of polynomial-basedsignatures that is a fraction 1/256 of a total scan space of possiblepolynomial-based signature values, the subset inclusion characteristicmay specify that application of a modulo 256 arithmetic operation to thepolynomial-based signatures results in a particular value.

3. Determine the size of the page identifier to be utilized in thededuplication estimate table. For example, the above-noted SHA1 hashfunction produces a 20-byte content-based signature, but the pageidentifier is selected as a number of initial bytes of the content-basedsignature. In such cases, a page identifier that is five bytes in lengthwill yield a very low rate of false positives.

4. Scan the dataset by computing polynomial-based signatures for thepages. If a given polynomial-based signature satisfies the subsetinclusion characteristic, the page identifier of that page is added tothe deduplication estimate table with an initial counter value of one.If the page identifier is already in the table, its counter isincremented by one.

5. Upon completion of the scan, a partial deduplication estimate isgenerated for the subspace by summing the counter values and that resultis scaled to generate the deduplication estimate for the full dataset.

These and other operations carried out in conjunction with a process forgenerating deduplication estimates in the host device 102 areillustratively performed at least in part under the control of thededuplication control logic of module 112. Such operations utilize theassociated deduplication estimate tables as well as content-basedsignatures generated for respective pages by the content-based signaturecomputation module 114.

The host device 102 and storage systems 105 in the FIG. 1 embodiment areassumed to be implemented using at least one processing platform eachcomprising one or more processing devices each having a processorcoupled to a memory. Such processing devices can illustratively includeparticular arrangements of compute, storage and network resources.

The host device 102 and the storage systems 105 may be implemented onrespective distinct processing platforms, although numerous otherarrangements are possible. For example, in some embodiments at leastportions of the host device 102 and one or both of the storage systems105 are implemented on the same processing platform. The storage systems105 can therefore be implemented at least in part within at least oneprocessing platform that implements at least a portion of the hostdevice 102.

The term “processing platform” as used herein is intended to be broadlyconstrued so as to encompass, by way of illustration and withoutlimitation, multiple sets of processing devices and associated storagesystems that are configured to communicate over one or more networks.For example, distributed implementations of the system 100 are possible,in which certain components of the system reside in one data center in afirst geographic location while other components of the system reside inone or more other data centers in one or more other geographic locationsthat are potentially remote from the first geographic location. Thus, itis possible in some implementations of the system 100 for the hostdevice 102 and storage systems 105 to reside in different data centers.Numerous other distributed implementations of one or both of the hostdevice 102 and the storage systems 105 are possible. Accordingly, thestorage systems 105 can also be implemented in a distributed manneracross multiple data centers.

Additional examples of processing platforms utilized to implement hostdevices and/or storage systems in illustrative embodiments will bedescribed in more detail below in conjunction with FIGS. 6 and 7.

It is to be appreciated that these and other features of illustrativeembodiments are presented by way of example only, and should not beconstrued as limiting in any way.

Accordingly, different numbers, types and arrangements of systemcomponents such as host device 102, network 104, storage systems 105,storage devices 106, storage controllers 108 and datasets 110 can beused in other embodiments.

It should be understood that the particular sets of modules and othercomponents implemented in the system 100 as illustrated in FIG. 1 arepresented by way of example only. In other embodiments, only subsets ofthese components, or additional or alternative sets of components, maybe used, and such components may exhibit alternative functionality andconfigurations. Additional examples of systems implementingdeduplication estimate functionality will be described below.

FIG. 2 shows an information processing system 200 configured inaccordance with another illustrative embodiment. The informationprocessing system 200 comprises a computer system 201 that includes hostdevices 202-1, 202-2, . . . 202-N. The host devices 202 communicate overa network 204 with a storage system 205. The computer system 201 isassumed to comprise an enterprise computer system, cloud-based computersystem or other arrangement of multiple compute nodes associated withrespective users. The host devices 202 of the computer system 201 insome embodiments illustratively provide compute services such asexecution of one or more applications on behalf of each of one or moreusers associated with respective ones of the host devices 202.

Similar to the storage systems 105 of system 100, the storage system 205comprises storage devices 206, storage controller 208 and datasets 210.However, in this embodiment, the deduplication estimate generationfunctionality is implemented in the storage system 205, rather than inone of the host devices 202. Accordingly, the storage controller 208 inthis embodiment comprises modules 212 and 214, which are configured tooperate in substantially the same manner as that described above forrespective corresponding modules 112 and 114 of the host device 102 inthe system 100. The module 212 is assumed to implement CRC computationfunctionality or other types of functionality for computingpolynomial-based signatures in the storage controller 208 of system 200.

In some embodiments, functionality for deduplication estimate generationand associated automated processing based at least in part on theresulting deduplication estimates can be implemented partially in a hostdevice and partially in a storage system. Accordingly, illustrativeembodiments are not limited to arrangements in which all suchfunctionality is implemented in a host device or a storage system, andtherefore encompass various hybrid arrangements in which thefunctionality is distributed over one or more host devices and one ormore storage systems, each comprising one or more processing devices.

The operation of the information processing systems 100 and 200 will nowbe described in further detail with reference to the flow diagram of theillustrative embodiment of FIG. 3. The process as shown includes steps300 through 320, and is suitable for use in systems 100 and 200 but ismore generally applicable to other types of information processingsystems in which a host device or storage system is configured togenerate a deduplication estimate for a dataset and to take one or moreautomated actions based at least in part on the generated deduplicationestimate. The steps are illustratively performed at least in part underthe control of the deduplication control logic implemented in module 112in host device 102 of system 100 or in module 212 in storage controller208 of system 200.

In step 300, a dataset is identified. The identified dataset is adataset to be scanned to generate a deduplication estimate for thatdataset. The dataset is illustratively one of a plurality of datasetsstored in a storage system, such as one of the datasets 110-1 or 110-2stored in respective storage systems 105-1 and 105-2 of system 100, orone or the datasets 210 stored in the storage system 205 of system 200.The identified dataset illustratively comprises a set of one or moreLUNs or other logical storage volumes of the storage system.

In step 302, a subset inclusion characteristic is designated to beutilized in the scan. The designated subset inclusion characteristicillustratively specifies that application of a designated function to apolynomial-based signature computed for a given page of the datasetproduces a particular result. For example, in some embodiments, thepolynomial-based signature of the page comprises an n-bit CRC value,such as a 32-bit CRC value, and the designated subset inclusioncharacteristic specifies that performing a designated modulo arithmeticoperation on the n-bit CRC value produces a particular value. Othertypes of polynomial-based signatures can be used in other embodiments.The subset inclusion characteristic effectively establishes a particularsubspace of a total scan space comprising all possible polynomial-basedsignatures. The ratio of the subspace established by the subsetinclusion characteristic to the total scan space of all possiblepolynomial-based signatures more particularly provides a sampling ratioin the FIG. 3 process for the identified dataset.

In step 304, a particular unscanned page of the identified dataset isselected. The term “unscanned page” in this context simply means thatthe page has not yet been scanned in conjunction with the current scanof the dataset, although it may have been previously scanned inconjunction with prior scans. In a first pass through step 304, allpages of the dataset are unscanned and so the selected unscanned pagecan be any page of the dataset, while in a second pass through step 304,all pages of the dataset other than the particular page selected in thefirst pass through step 304 remain unscanned, and so on. At a final passthrough step 304, there is only one remaining unscanned page of thedataset, and that page is selected.

In step 306, a polynomial-based signature is computed for the particularpage selected in step 304. The polynomial-based signature isillustratively computed using an n-bit CRC as indicated above. Such CRCcomputation functionality is assumed to be implemented as part of thededuplication control logic of module 112 of host device 102 in system100 or module 212 of storage controller 208 in system 200.

As mentioned previously, the polynomial-based signatures aresubstantially less computationally expensive to generate thancontent-based signatures. For example, in some embodiments, thepolynomial-based signatures are generated from a particular designatedportion of each page, such as the first 100 bytes of the page, using arelatively simple operation such as a 32-bit CRC, while thecontent-based signatures are computed by applying a relatively complexoperation such as SHA1 to the entire content of the page. The presentembodiment avoids the need to compute a computationally complexcontent-based signature for each page of a dataset in generating adeduplication estimate for that dataset.

In step 308, a determination is made as to whether or not thepolynomial-based signature computed in step 306 satisfies the designatedsubset inclusion characteristic. If the polynomial-based signaturesatisfies the designated subset inclusion characteristic, the processmoves to step 310, and otherwise moves to step 314 as indicated.

In step 310, a content-based signature is computed for the page. Thecontent-based signature is illustratively computed using SHA1 or anothertype of secure hashing algorithm. Such a computation is assumed to beperformed utilizing content-based signature computation module 114 ofhost device 102 in system 100 or content-based signature computationmodule 214 of storage controller 208 in system 200.

In step 312, a corresponding entry of a deduplication estimate table forthe dataset is updated using the content-based signature computed instep 310. The process then moves to step 314. The deduplication estimatetable illustratively comprises a plurality of entries for respectiveones of the pages, with each of the entries being configured to includea page identifier that comprises less than an entire content-basedsignature of its corresponding page.

An example of an arrangement of this type is shown in FIG. 4, whichshows a deduplication estimate table 400 for a given dataset i in anillustrative embodiment. The page identifier in a corresponding entry ofthe deduplication estimate table 400 comprises a particular number ofinitial bytes of the content-based signature of that page. Each of theentries of the deduplication estimate table 400 in this exampletherefore comprises a page identifier that includes initial bytes of thecontent-based signature of that page, as well as a correspondingcounter. In this example, there are C entries in the table, and thus thecounters are denoted Counter 1, Counter 2, . . . Counter C as indicatedin the figure.

Other arrangements of table fields can be used, and the term “table” asused herein is intended to be broadly construed so as to encompassnumerous alternative data structures for maintaining information used ingenerating a deduplication estimate.

In some embodiments, the page identifier comprises a designated numberof initial bytes of the content-based signature for that page, where thedesignated number is greater than or equal to four. As a more particularexample, the designated number may be five, such that the pageidentifier comprises five initial bytes. Other page identifier lengthsand configurations may be used in other embodiments.

Also, a processing device as disclosed herein may be configured toadjust at least one of an amount of computational resources and anamount of memory resources to be utilized in the scan of the identifieddataset at least in part by altering the length of the page identifier.

Additionally or alternatively, the processing device may be configuredto select a length of the page identifier in order to achieve a rate offalse positives in the resulting deduplication estimate that is lessthan a specified maximum rate of false positives.

The updating of the corresponding entry of the deduplication estimatetable for a given one of the pages of the dataset in step 312illustratively proceeds in the following manner. If a page identifier ofthe given page is not already present in the deduplication estimatetable, the page identifier is inserted into the deduplication estimatetable and an associated counter is set to an initial value, typically avalue of one. On the other hand, if the page identifier of the givenpage is already present in the deduplication estimate table, itsassociated counter is incremented. It is to be appreciated that othertypes of updating processes and deduplication estimate tableconfigurations may be used in other embodiments.

Accordingly, the particular table configuration shown in FIG. 4 is onlyan example, and should not be construed as limiting in any way. Also,the term “table” as used herein is intended to be broadly construed, anda given table can be implemented using a wide variety of different datastructures or other storage arrangements.

In step 314, a determination is made as to whether or not there is atleast one additional page to be scanned in the identified dataset. Ifthere is at least one additional page to be scanned, the process returnsto step 304 to select an unscanned page for scanning. Otherwise, theprocess moves to step 316.

In step 316, the contents of the deduplication estimate table areutilized to generate a deduplication estimate for dataset. For example,a partial deduplication estimate may be computed based at least in parton values of the counters associated with respective entries of thededuplication estimate table. This illustratively involves summing thevalues of the counters, although other functions of the counter valuescould be computed in other embodiments. The resulting summation ofcounter values provides a partial deduplication estimate indicative ofthe amount of storage capacity reduction that can be achieved byapplying a deduplication operation to the dataset. This partialdeduplication estimate is then scaled to obtain the deduplicationestimate for the dataset. The scaling illustratively involvesmultiplying the partial deduplication estimate by an inverse of thesampling ratio, where the sampling ratio as described above is given bythe ratio of the subspace of polynomial-based signatures defined by thesubset inclusion characteristic to the total scan space of all possiblepolynomial-based signatures. Again, other types of scaling can be usedin other embodiments.

In step 318, a determination is made as to whether or not there is atleast one additional dataset to be scanned to generate a deduplicationestimate for that dataset. If there is at least one additional datasetto be scanned, the process returns to step 300 to identify anotherdataset for scanning. Otherwise, the process ends in step 320.

The deduplication estimates generated using the FIG. 3 process areutilized to perform one or more automated operations in at least one ofa host device and a storage system. For example, the processing devicemay be configured to adjust one or more characteristics of a storageconfiguration of a given dataset based at least in part on thededuplication estimate generated for that dataset.

This may include, for example, determining whether to store the datasetin a first storage system that does not have deduplication functionalityor in a second storage system that has deduplication functionality.

As another example, the processing device may generate multiple distinctestimates for different groupings of data into datasets in order todetermine a particular manner in which to group the data to achievemaximum deduplication in a given storage system or in conjunction withdetermination of an appropriate migration of the dataset from a firststorage system of a first type to a second storage system of a secondtype.

In some embodiments, the processing device is configured to generatemultiple deduplication estimates for respective different datasets, andto select a particular one of the datasets for deduplication based atleast in part on their respective deduplication estimates. For example,the dataset having the highest-valued deduplication estimate may beprioritized for deduplication in a storage system over other datasetshaving lower-valued deduplication estimates. These and numerous otherautomated actions may be taken utilizing deduplication estimatesgenerated using the FIG. 3 process.

In some embodiments, the deduplication estimates are utilized in anartificial intelligence (AI) engine that controls migration of datasetsor other storage configuration characteristics of datasets.

The particular processing operations and other system functionalitydescribed in conjunction with the flow diagram of FIG. 3 are presentedby way of illustrative example only, and should not be construed aslimiting the scope of the disclosure in any way. Alternative embodimentscan use other types of processing operations for generatingdeduplication estimates. For example, the ordering of the process stepsmay be varied in other embodiments, or certain steps may be performed atleast in part concurrently with one another rather than serially. Also,one or more of the process steps may be repeated periodically, ormultiple instances of the process can be performed in parallel with oneanother in order to implement a plurality of different deduplicationestimation processes for respective different datasets or for differentstorage systems or portions thereof within a given informationprocessing system.

Functionality such as that described in conjunction with the flowdiagram of FIG. 3 can be implemented at least in part in the form of oneor more software programs stored in memory and executed by a processorof a processing device such as a computer or server. As will bedescribed below, a memory or other storage device having executableprogram code of one or more software programs embodied therein is anexample of what is more generally referred to herein as a“processor-readable storage medium.”

For example, a host device such as host device 102 or a storagecontroller such as storage controller 208 that is configured to controlperformance of one or more steps of the FIG. 3 process in itscorresponding system 100 or 200 can be implemented as part of what ismore generally referred to herein as a processing platform comprisingone or more processing devices each comprising a processor coupled to amemory. A given such processing device may correspond to one or morevirtual machines or other types of virtualization infrastructure such asDocker containers or Linux containers (LXCs). The host device 102 ofsystem 100 or the storage controller 208 of system 200, as well as othersystem components, may be implemented at least in part using processingdevices of such processing platforms. For example, in a distributedimplementation of the storage controller 208, respective distributedmodules of such a storage controller can be implemented in respectivecontainers running on respective ones of the processing devices of aprocessing platform.

In some embodiments, the storage system comprises an XtremIO™ storagearray or other type of content addressable storage system suitablymodified to incorporate deduplication estimate generation techniques asdisclosed herein.

An illustrative embodiment of such a content addressable storage systemwill now be described with reference to FIG. 5. In this embodiment, acontent addressable storage system 505 comprises a plurality of storagedevices 506 and an associated storage controller 508. The contentaddressable storage system 505 may be viewed as a particularimplementation of the storage system 205, and accordingly is assumed tobe coupled to host devices 202 of computer system 201 via network 204within information processing system 200.

The storage controller 508 in the present embodiment is configured toimplement deduplication estimate generation functionality of the typepreviously described in conjunction with FIGS. 1 through 4.

The storage controller 508 includes distributed modules 512 and 514,which are configured to operate in a manner similar to that describedabove for respective corresponding modules 112, 212 and 114, 214.

The content addressable storage system 505 in the FIG. 5 embodiment isimplemented as at least a portion of a clustered storage system andincludes a plurality of storage nodes 515 each comprising acorresponding subset of the storage devices 506. Other clustered storagesystem arrangements comprising multiple storage nodes can be used inother embodiments. A given clustered storage system may include not onlystorage nodes 515 but also additional storage nodes coupled to network204. Alternatively, such additional storage nodes may be part of anotherclustered storage system of the system 200. Each of the storage nodes515 of the storage system 505 is assumed to be implemented using atleast one processing device comprising a processor coupled to a memory.

The storage controller 508 of the content addressable storage system 505is implemented in a distributed manner so as to comprise a plurality ofdistributed storage controller components implemented on respective onesof the storage nodes 515. The storage controller 508 is therefore anexample of what is more generally referred to herein as a “distributedstorage controller.” In subsequent description herein, the storagecontroller 508 may be more particularly referred to as a distributedstorage controller.

Each of the storage nodes 515 in this embodiment further comprises a setof processing modules configured to communicate over one or morenetworks with corresponding sets of processing modules on other ones ofthe storage nodes 515. The sets of processing modules of the storagenodes 515 collectively comprise at least a portion of the distributedstorage controller 508 of the content addressable storage system 505.

The modules of the distributed storage controller 508 in the presentembodiment more particularly comprise different sets of processingmodules implemented on each of the storage nodes 515. The set ofprocessing modules of each of the storage nodes 515 comprises at least acontrol module 508C, a data module 508D and a routing module 508R. Thedistributed storage controller 508 further comprises one or moremanagement (“MGMT”) modules 508M. For example, only a single one of thestorage nodes 515 may include a management module 508M. It is alsopossible that management modules 508M may be implemented on each of atleast a subset of the storage nodes 515.

Each of the storage nodes 515 of the storage system 505 thereforecomprises a set of processing modules configured to communicate over oneor more networks with corresponding sets of processing modules on otherones of the storage nodes. A given such set of processing modulesimplemented on a particular storage node illustratively includes atleast one control module 508C, at least one data module 508D and atleast one routing module 508R, and possibly a management module 508M.These sets of processing modules of the storage nodes collectivelycomprise at least a portion of the distributed storage controller 508.

Communication links may be established between the various processingmodules of the distributed storage controller 508 using well-knowncommunication protocols such as IP and Transmission Control Protocol(TCP). For example, respective sets of IP links used in data transferand corresponding messaging could be associated with respectivedifferent ones of the routing modules 508R.

Although shown as separate modules of the distributed storage controller508, the modules 512 and 514 in the present embodiment are assumed to bedistributed at least in part over at least a subset of the other modules508C, 508D, 508R and 508M of the storage controller 508. Accordingly, atleast portions of the deduplication estimate generation functionality ofthe modules 512 and 514 may be implemented in one or more of the othermodules of the storage controller 508. In other embodiments, the modules512 and 514 may be implemented as stand-alone modules of the storagecontroller 508.

The storage devices 506 are configured to store metadata pages 520 anduser data pages 522, and may also store additional information notexplicitly shown such as checkpoints and write journals. The metadatapages 520 and the user data pages 522 are illustratively stored inrespective designated metadata and user data areas of the storagedevices 506. Accordingly, metadata pages 520 and user data pages 522 maybe viewed as corresponding to respective designated metadata and userdata areas of the storage devices 506.

A given “page” as the term is broadly used herein should not be viewedas being limited to any particular range of fixed sizes. In someembodiments, a page size of 8 kilobytes (KB) is used, but this is by wayof example only and can be varied in other embodiments. For example,page sizes of 4 KB, 16 KB or other values can be used. Accordingly,illustrative embodiments can utilize any of a wide variety ofalternative paging arrangements for organizing the metadata pages 520and the user data pages 522.

The user data pages 522 are part of a plurality of LUNs configured tostore files, blocks, objects or other arrangements of data, each alsogenerally referred to herein as a “data item,” on behalf of usersassociated with host devices 202. Each such LUN may comprise particularones of the above-noted pages of the user data area. The user datastored in the user data pages 522 can include any type of user data thatmay be utilized in the system 200. The term “user data” herein istherefore also intended to be broadly construed.

A given dataset for which a deduplication estimate is generated usingmodules 512 and 514 illustratively comprises a set of LUNs, eachincluding multiple ones of the user data pages 522 stored in storagedevices 506.

The content addressable storage system 505 in the embodiment of FIG. 5is configured to generate hash metadata providing a mapping betweencontent-based digests of respective ones of the user data pages 522 andcorresponding physical locations of those pages in the user data area.Content-based digests generating using hash functions are also referredto herein as “hash digests.” Such hash digests or other types ofcontent-based digests are examples of what are more generally referredto herein as “content-based signatures” of the respective user datapages 522. The hash metadata generated by the content addressablestorage system 505 is illustratively stored as metadata pages 520 in themetadata area. The generation and storage of the hash metadata isassumed to be performed under the control of the storage controller 508.

Each of the metadata pages 520 characterizes a plurality of the userdata pages 522. For example, a given set of user data pages representinga portion of the user data pages 522 illustratively comprises aplurality of user data pages denoted User Data Page 1, User Data Page 2,. . . User Data Page n. It should be noted that usage of the variable nin this user data page context is unrelated to its usage elsewhereherein in the content of an n-bit CRC.

Each of the user data pages 522 in this example is characterized by aLUN identifier, an offset and a content-based signature. Thecontent-based signature is generated as a hash function of content ofthe corresponding user data page. Illustrative hash functions that maybe used to generate the content-based signature include the above-notedSHA1 hash function, or other secure hashing algorithms known to thoseskilled in the art. The content-based signature is utilized to determinethe location of the corresponding user data page within the user dataarea of the storage devices 506.

Each of the metadata pages 520 in the present embodiment is assumed tohave a signature that is not content-based. For example, the metadatapage signatures may be generated using hash functions or other signaturegeneration algorithms that do not utilize content of the metadata pagesas input to the signature generation algorithm. Also, each of themetadata pages is assumed to characterize a different set of the userdata pages.

A given set of metadata pages representing a portion of the metadatapages 520 in an illustrative embodiment comprises metadata pages denotedMetadata Page 1, Metadata Page 2,. . . Metadata Page m, havingrespective signatures denoted Signature 1, Signature 2, . . . Signaturem. Each such metadata page characterizes a different set of n user datapages. For example, the characterizing information in each metadata pagecan include the LUN identifiers, offsets and content-based signaturesfor each of the n user data pages that are characterized by thatmetadata page. It is to be appreciated, however, that the user data andmetadata page configurations described above are examples only, andnumerous alternative user data and metadata page configurations can beused in other embodiments.

Ownership of a user data logical address space within the contentaddressable storage system 505 is illustratively distributed among thecontrol modules 508C.

The deduplication estimate generation functionality provided by modules512 and 514 in this embodiment is assumed to be distributed acrossmultiple distributed processing modules, including at least a subset ofthe processing modules 508C, 508D, 508R and 508M of the distributedstorage controller 508.

For example, the management module 508M of the storage controller 508may include deduplication control logic that engages correspondingdeduplication control logic instances in all of the control modules 508Cand routing modules 508R in order to implement a deduplication estimategeneration process.

In some embodiments, the content addressable storage system 505comprises an XtremIO™ storage array suitably modified to incorporatetechniques for generation of deduplication estimates and performance ofautomated functions based at least in part on those estimates asdisclosed herein.

In arrangements of this type, the control modules 508C, data modules508D and routing modules 508R of the distributed storage controller 508illustratively comprise respective C-modules, D-modules and R-modules ofthe XtremIO™ storage array. The one or more management modules 508M ofthe distributed storage controller 508 in such arrangementsillustratively comprise a system-wide management module (“SYM module”)of the XtremIO™ storage array, although other types and arrangements ofsystem-wide management modules can be used in other embodiments.Accordingly, deduplication estimate generation functionality in someembodiments is implemented under the control of at least one system-widemanagement module of the distributed storage controller 508, utilizingthe C-modules, D-modules and R-modules of the XtremIO™ storage array.

In the above-described XtremIO™ storage array example, each user datapage has a fixed size such as 8 KB and its content-based signature is a20-byte signature generated using an SHA1 hash function. Also, each pagehas a LUN identifier and an offset, and so is characterized by <lun_id,offset, signature>.

The content-based signature in the present example comprises acontent-based digest of the corresponding data page. Such acontent-based digest is more particularly referred to as a “hash digest”of the corresponding data page, as the content-based signature isillustratively generated by applying a hash function such as SHA1 to thecontent of that data page. The full hash digest of a given data page isgiven by the above-noted 20-byte signature. The hash digest may berepresented by a corresponding “hash handle,” which in some cases maycomprise a particular portion of the hash digest. The hash handleillustratively maps on a one-to-one basis to the corresponding full hashdigest within a designated cluster boundary or other specified storageresource boundary of a given storage system. In arrangements of thistype, the hash handle provides a lightweight mechanism for uniquelyidentifying the corresponding full hash digest and its associated datapage within the specified storage resource boundary. The hash digest andhash handle are both considered examples of “content-based signatures”as that term is broadly used herein.

Examples of techniques for generating and processing hash handles forrespective hash digests of respective data pages are disclosed in U.S.Pat. No. 9,208,162, entitled “Generating a Short Hash Handle,” and U.S.Pat. No. 9,286,003, entitled “Method and Apparatus for Creating a ShortHash Handle Highly Correlated with a Globally-Unique Hash Signature,”both of which are incorporated by reference herein.

As mentioned previously, storage controller components in an XtremIO™storage array illustratively include C-module, D-module and R-modulecomponents. For example, separate instances of such components can beassociated with each of a plurality of storage nodes in a clusteredstorage system implementation.

The distributed storage controller in this example is configured togroup consecutive pages into page groups, to arrange the page groupsinto slices, and to assign the slices to different ones of theC-modules. For example, if there are 1024 slices distributed evenlyacross the C-modules, and there are a total of 16 C-modules in a givenimplementation, each of the C-modules “owns” 1024/16=64 slices. In sucharrangements, different ones of the slices are assigned to differentones of the control modules 508C such that control of the slices withinthe storage controller 508 of the storage system 505 is substantiallyevenly distributed over the control modules 508C of the storagecontroller 508.

The D-module allows a user to locate a given user data page based on itssignature. Each metadata page also has a size of 8 KB and includesmultiple instances of the <lun_id, offset, signature> for respectiveones of a plurality of the user data pages. Such metadata pages areillustratively generated by the C-module but are accessed using theD-module based on a metadata page signature.

The metadata page signature in this embodiment is a 20-byte signaturebut is not based on the content of the metadata page. Instead, themetadata page signature is generated based on an 8-byte metadata pageidentifier that is a function of the LUN identifier and offsetinformation of that metadata page.

If a user wants to read a user data page having a particular LUNidentifier and offset, the corresponding metadata page identifier isfirst determined, then the metadata page signature is computed for theidentified metadata page, and then the metadata page is read using thecomputed signature. In this embodiment, the metadata page signature ismore particularly computed using a signature generation algorithm thatgenerates the signature to include a hash of the 8-byte metadata pageidentifier, one or more ASCII codes for particular predeterminedcharacters, as well as possible additional fields. The last bit of themetadata page signature may always be set to a particular logic value soas to distinguish it from the user data page signature in which the lastbit may always be set to the opposite logic value.

The metadata page signature is used to retrieve the metadata page viathe D-module. This metadata page will include the <lun_id, offset,signature> for the user data page if the user page exists. The signatureof the user data page is then used to retrieve that user data page, alsovia the D-module.

Write requests processed in the content addressable storage system 505each illustratively comprise one or more I/O operations directing thatat least one data item of the storage system 505 be written to in aparticular manner. A given write request is illustratively received inthe storage system 505 from a host device, illustratively one of thehost devices 202. In some embodiments, a write request is received inthe distributed storage controller 508 of the storage system 505, anddirected from one processing module to another processing module of thedistributed storage controller 508. For example, a received writerequest may be directed from a routing module 508R of the distributedstorage controller 508 to a particular control module 508C of thedistributed storage controller 508. Other arrangements for receiving andprocessing write requests from one or more host devices can be used.

The term “write request” as used herein is intended to be broadlyconstrued, so as to encompass one or more I/O operations directing thatat least one data item of a storage system be written to in a particularmanner. A given write request is illustratively received in a storagesystem from a host device.

In the XtremIO™ context, the C-modules, D-modules and R-modules of thestorage nodes 515 communicate with one another over a high-speedinternal network such as an InfiniBand network. The C-modules, D-modulesand R-modules coordinate with one another to accomplish various I/Oprocessing tasks.

The write requests from the host devices identify particular data pagesto be written in the storage system 505 by their corresponding logicaladdresses each comprising a LUN ID and an offset.

As noted above, a given one of the content-based signaturesillustratively comprises a hash digest of the corresponding data page,with the hash digest being generated by applying a hash function to thecontent of that data page. The hash digest may be uniquely representedwithin a given storage resource boundary by a corresponding hash handle.

The storage system 505 utilizes a two-level mapping process to maplogical block addresses to physical block addresses. The first level ofmapping uses an address-to-hash (“A2H”) table and the second level ofmapping uses a hash metadata (“HMD”) table, with the A2H and HMD tablescorresponding to respective logical and physical layers of thecontent-based signature mapping within the storage system 505.

The first level of mapping using the A2H table associates logicaladdresses of respective data pages with respective content-basedsignatures of those data pages. This is also referred to logical layermapping.

The second level of mapping using the HMD table associates respectiveones of the content-based signatures with respective physical storagelocations in one or more of the storage devices 506. This is alsoreferred to as physical layer mapping.

For a given write request, both of the corresponding HMD and A2H tablesare updated in conjunction with the processing of that write request.

The A2H and HMD tables described above are examples of what are moregenerally referred to herein as “mapping tables” of respective first andsecond distinct types. Other types and arrangements of mapping tables orother content-based signature mapping information may be used in otherembodiments.

The logical block addresses or LBAs of a logical layer of the storagesystem 505 correspond to respective physical blocks of a physical layerof the storage system 505. The user data pages of the logical layer areorganized by LBA and have reference via respective content-basedsignatures to particular physical blocks of the physical layer.

Each of the physical blocks has an associated reference count that ismaintained within the storage system 505. The reference count for agiven physical block indicates the number of logical blocks that pointto that same physical block.

In releasing logical address space in the storage system, adereferencing operation is generally executed for each of the LBAs beingreleased. More particularly, the reference count of the correspondingphysical block is decremented. A reference count of zero indicates thatthere are no longer any logical blocks that reference the correspondingphysical block, and so that physical block can be released.

It should also be understood that the particular arrangement of storagecontroller processing modules 508C, 508D, 508R and 508M as shown in theFIG. 5 embodiment is presented by way of example only. Numerousalternative arrangements of processing modules of a distributed storagecontroller may be used to implement deduplication estimate generationfunctionality in a clustered storage system in other embodiments.

Additional examples of content addressable storage functionalityimplemented in some embodiments by control modules 508C, data modules508D, routing modules 508R and management module(s) 508M of distributedstorage controller 508 can be found in U.S. Pat. No. 9,104,326, entitled“Scalable Block Data Storage Using Content Addressing,” which isincorporated by reference herein. Alternative arrangements of these andother storage node processing modules of a distributed storagecontroller in a content addressable storage system can be used in otherembodiments.

Illustrative embodiments of host devices or storage systems withdeduplication estimate generation functionality as disclosed herein canprovide a number of significant advantages relative to conventionalarrangements.

For example, some embodiments provide techniques for efficientgeneration of deduplication estimates for datasets of a storage systemthrough utilization of polynomial-based signature subspaces in scanningpages of the datasets.

The polynomial-based signatures are substantially less computationallyexpensive to generate than content-based signatures. Illustrativeembodiments advantageously avoid the need to compute content-basedsignatures for each page of a dataset in generating a deduplicationestimate for that dataset.

Functionality for deduplication estimate generation and associatedautomated processing based at least in part on the resultingdeduplication estimates can be implemented in a host device, in astorage system, or partially in a host device and partially in a storagesystem.

Illustrative embodiments can be configured to generate highly accuratededuplication estimates in a manner that does not require large amountsof host device or storage system memory. For example, these embodimentsavoid the need to store a complete content-based signature for each datapage encountered during a scan, thereby conserving valuable memoryresources of the host device or storage system.

Some embodiments can be dynamically reconfigured to trade offperformance measures such as rate of false positives with amounts ofcomputational and memory resources consumed by the scan.

These and other embodiments can considerably reduce the amounts ofcomputational and memory resources that are required to generatededuplication estimates, thereby leading to improved deduplicationdecisions and associated improvements in system performance.

It is to be appreciated that the particular advantages described aboveand elsewhere herein are associated with particular illustrativeembodiments and need not be present in other embodiments. Also, theparticular types of information processing system features andfunctionality as illustrated in the drawings and described above areexemplary only, and numerous other arrangements may be used in otherembodiments.

Illustrative embodiments of processing platforms utilized to implementhost devices and storage systems with deduplication estimate generationfunctionality will now be described in greater detail with reference toFIGS. 6 and 7. Although described in the context of system 100, theseplatforms may also be used to implement at least portions of otherinformation processing systems in other embodiments.

FIG. 6 shows an example processing platform comprising cloudinfrastructure 600. The cloud infrastructure 600 comprises a combinationof physical and virtual processing resources that may be utilized toimplement at least a portion of the information processing system 100.The cloud infrastructure 600 comprises multiple virtual machines (VMs)and/or container sets 602-1, 602-2, . . . 602-L implemented usingvirtualization infrastructure 604. The virtualization infrastructure 604runs on physical infrastructure 605, and illustratively comprises one ormore hypervisors and/or operating system level virtualizationinfrastructure. The operating system level virtualization infrastructureillustratively comprises kernel control groups of a Linux operatingsystem or other type of operating system.

The cloud infrastructure 600 further comprises sets of applications610-1, 610-2, . . . 610-L running on respective ones of theVMs/container sets 602-1, 602-2, . . . 602-L under the control of thevirtualization infrastructure 604. The VMs/container sets 602 maycomprise respective VMs, respective sets of one or more containers, orrespective sets of one or more containers running in VMs.

In some implementations of the FIG. 6 embodiment, the VMs/container sets602 comprise respective VMs implemented using virtualizationinfrastructure 604 that comprises at least one hypervisor. Suchimplementations can provide deduplication estimate generationfunctionality of the type described above for one or more processesrunning on a given one of the VMs. For example, each of the VMs canimplement deduplication control logic and associated deduplicationestimate tables for providing deduplication estimate generationfunctionality for one or more processes running on that particular VM.

An example of a hypervisor platform that may be used to implement ahypervisor within the virtualization infrastructure 604 is the VMware®vSphere® which may have an associated virtual infrastructure managementsystem such as the VMware® vCenter™. The underlying physical machinesmay comprise one or more distributed processing platforms that includeone or more storage systems.

In other implementations of the FIG. 6 embodiment, the VMs/containersets 602 comprise respective containers implemented using virtualizationinfrastructure 604 that provides operating system level virtualizationfunctionality, such as support for Docker containers running on baremetal hosts, or Docker containers running on VMs. The containers areillustratively implemented using respective kernel control groups of theoperating system. Such implementations can provide deduplicationestimate generation functionality of the type described above for one ormore processes running on different ones of the containers. For example,a container host device supporting multiple containers of one or morecontainer sets can implement one or more instances of deduplicationcontrol logic and associated deduplication estimate tables for use ingenerating deduplication estimates.

As is apparent from the above, one or more of the processing modules orother components of system 100 may each run on a computer, server,storage device or other processing platform element. A given suchelement may be viewed as an example of what is more generally referredto herein as a “processing device.” The cloud infrastructure 600 shownin FIG. 6 may represent at least a portion of one processing platform.Another example of such a processing platform is processing platform 700shown in FIG. 7.

The processing platform 700 in this embodiment comprises a portion ofsystem 100 and includes a plurality of processing devices, denoted702-1, 702-2, 702-3, . . . 702-K, which communicate with one anotherover a network 704.

The network 704 may comprise any type of network, including by way ofexample a global computer network such as the Internet, a WAN, a LAN, asatellite network, a telephone or cable network, a cellular network, awireless network such as a WiFi or WiMAX network, or various portions orcombinations of these and other types of networks.

The processing device 702-1 in the processing platform 700 comprises aprocessor 710 coupled to a memory 712.

The processor 710 may comprise a microprocessor, a microcontroller, anapplication-specific integrated circuit (ASIC), a field-programmablegate array (FPGA) or other type of processing circuitry, as well asportions or combinations of such circuitry elements.

The memory 712 may comprise random access memory (RAM), read-only memory(ROM), flash memory or other types of memory, in any combination. Thememory 712 and other memories disclosed herein should be viewed asillustrative examples of what are more generally referred to as“processor-readable storage media” storing executable program code ofone or more software programs.

Articles of manufacture comprising such processor-readable storage mediaare considered illustrative embodiments. A given such article ofmanufacture may comprise, for example, a storage array, a storage diskor an integrated circuit containing RAM, ROM, flash memory or otherelectronic memory, or any of a wide variety of other types of computerprogram products. The term “article of manufacture” as used hereinshould be understood to exclude transitory, propagating signals.Numerous other types of computer program products comprisingprocessor-readable storage media can be used.

Also included in the processing device 702-1 is network interfacecircuitry 714, which is used to interface the processing device with thenetwork 704 and other system components, and may comprise conventionaltransceivers.

The other processing devices 702 of the processing platform 700 areassumed to be configured in a manner similar to that shown forprocessing device 702-1 in the figure.

Again, the particular processing platform 700 shown in the figure ispresented by way of example only, and system 100 may include additionalor alternative processing platforms, as well as numerous distinctprocessing platforms in any combination, with each such platformcomprising one or more computers, servers, storage devices or otherprocessing devices.

For example, other processing platforms used to implement illustrativeembodiments can comprise converged infrastructure such as VxRail™,VxRack™, VxRack™ FLEX, VxBlock™, or Vblock® converged infrastructurefrom VCE, the Virtual Computing Environment Company, now the ConvergedPlatform and Solutions Division of Dell EMC.

It should therefore be understood that in other embodiments differentarrangements of additional or alternative elements may be used. At leasta subset of these elements may be collectively implemented on a commonprocessing platform, or each such element may be implemented on aseparate processing platform.

As indicated previously, components of an information processing systemas disclosed herein can be implemented at least in part in the form ofone or more software programs stored in memory and executed by aprocessor of a processing device. For example, at least portions of thededuplication estimate generation functionality of one or morecomponents of a host device or storage system as disclosed herein areillustratively implemented in the form of software running on one ormore processing devices.

It should again be emphasized that the above-described embodiments arepresented for purposes of illustration only. Many variations and otheralternative embodiments may be used. For example, the disclosedtechniques are applicable to a wide variety of other types ofinformation processing systems, host devices, storage systems, storagenodes, storage devices, storage controllers, deduplication estimategeneration processes and associated control logic and tables. Also, theparticular configurations of system and device elements and associatedprocessing operations illustratively shown in the drawings can be variedin other embodiments. Moreover, the various assumptions made above inthe course of describing the illustrative embodiments should also beviewed as exemplary rather than as requirements or limitations of thedisclosure. Numerous other alternative embodiments within the scope ofthe appended claims will be readily apparent to those skilled in theart.

What is claimed is:
 1. An apparatus comprising: at least one processingdevice comprising a processor coupled to a memory; the processing devicebeing configured: to identify a dataset to be scanned to generate adeduplication estimate for that dataset; to designate a subset inclusioncharacteristic to be utilized in the scan; for each of a plurality ofpages of the dataset, to scan the page by: computing a polynomial-basedsignature for the page; determining whether or not the polynomial-basedsignature satisfies the designated subset inclusion characteristic; andresponsive to the polynomial-based signature satisfying the designatedsubset inclusion characteristic, computing a content-based signature forthe page and updating a corresponding entry of a deduplication estimatetable for the dataset based at least in part on the content-basedsignature; and to generate the deduplication estimate for the datasetbased at least in part on contents of the deduplication estimate table.2. The apparatus of claim 1 wherein the processing device is implementedin one of: a host device configured to communicate over a network with astorage system that stores the dataset; and the storage system thatstores the dataset.
 3. The apparatus of claim 1 wherein the datasetcomprises a set of one or more logical storage volumes of a storagesystem.
 4. The apparatus of claim 1 wherein updating a correspondingentry of the deduplication estimate table for a given one of the pagesof the dataset comprises one of the following operations (i) and (ii):(i) responsive to a page identifier of the given page not already beingpresent in the deduplication estimate table, inserting the pageidentifier into the deduplication estimate table and setting anassociated counter to an initial value; and (ii) responsive to the pageidentifier already being present in the deduplication estimate table,incrementing its associated counter.
 5. The apparatus of claim 1 whereinthe deduplication estimate table comprises a plurality of entries forrespective ones of the pages and wherein each of the entries isconfigured to include a page identifier that comprises less than anentire content-based signature of its corresponding page.
 6. Theapparatus of claim 5 wherein the processing device is configured toadjust at least one of an amount of computational resources and anamount of memory resources to be utilized in the scan of the dataset atleast in part by altering a length of the page identifier.
 7. Theapparatus of claim 5 wherein the processing device is configured toselect a length of the page identifier in order to achieve a rate offalse positives in the deduplication estimate that is less than aspecified maximum rate of false positives.
 8. The apparatus of claim 1wherein the designated subset inclusion characteristic specifies thatapplication of a designated function to the polynomial-based signatureproduces a particular result.
 9. The apparatus of claim 1 wherein thepolynomial-based signature comprises an n-bit cyclic redundancy check(CRC) value.
 10. The apparatus of claim 9 wherein the n-bit CRC valuecomprises a 32-bit CRC value.
 11. The apparatus of claim 9 wherein thedesignated subset inclusion characteristic specifies that performing adesignated modulo arithmetic operation on the n-bit CRC value produces aparticular value.
 12. The apparatus of claim 1 wherein generating thededuplication estimate for the dataset based at least in part oncontents of the deduplication estimate table further comprises:computing a partial deduplication estimate based at least in part onvalues of counters associated with respective entries of thededuplication estimate table; and scaling the partial deduplicationestimate to obtain the deduplication estimate for the dataset.
 13. Theapparatus of claim 1 wherein the processing device is configured toadjust one or more characteristics of a storage configuration of thedataset based at least in part on the deduplication estimate generatedfor the dataset.
 14. The apparatus of claim 1 wherein the processingdevice is configured: to generate one or more additional deduplicationestimates for respective ones of one or more additional datasets; and toselect a particular one of the datasets for deduplication based at leastin part on their respective deduplication estimates.
 15. A methodcomprising: identifying a dataset to be scanned to generate adeduplication estimate for that dataset; designating a subset inclusioncharacteristic to be utilized in the scan; for each of a plurality ofpages of the dataset, scanning the page by: computing a polynomial-basedsignature for the page; determining whether or not the polynomial-basedsignature satisfies the designated subset inclusion characteristic; andresponsive to the polynomial-based signature satisfying the designatedsubset inclusion characteristic, computing a content-based signature forthe page and updating a corresponding entry of a deduplication estimatetable for the dataset based at least in part on the content-basedsignature; and generating the deduplication estimate for the datasetbased at least in part on contents of the deduplication estimate table;wherein the method is implemented by at least one processing devicecomprising a processor coupled to a memory.
 16. The method of claim 15wherein the designated subset inclusion characteristic specifies thatapplication of a designated function to the polynomial-based signatureproduces a particular result.
 17. The method of claim 15 wherein thepolynomial-based signature comprises an n-bit cyclic redundancy check(CRC) value.
 18. A computer program product comprising a non-transitoryprocessor-readable storage medium having stored therein program code ofone or more software programs, wherein the program code when executed byat least one processing device causes said at least one processingdevice: to identify a dataset to be scanned to generate a deduplicationestimate for that dataset; to designate a subset inclusioncharacteristic to be utilized in the scan; for each of a plurality ofpages of the dataset, to scan the page by: computing a polynomial-basedsignature for the page; determining whether or not the polynomial-basedsignature satisfies the designated subset inclusion characteristic; andresponsive to the polynomial-based signature satisfying the designatedsubset inclusion characteristic, computing a content-based signature forthe page and updating a corresponding entry of a deduplication estimatetable for the dataset based at least in part on the content-basedsignature; and to generate the deduplication estimate for the datasetbased at least in part on contents of the deduplication estimate table.19. The computer program product of claim 18 wherein the designatedsubset inclusion characteristic specifies that application of adesignated function to the polynomial-based signature produces aparticular result.
 20. The computer program product of claim 18 whereinthe polynomial-based signature comprises an n-bit cyclic redundancycheck (CRC) value.